Research Study: AI Coding Assistants' Tool Recommendations Analysis
By
tin7in
Lightly browned and well buttered. A solid pick from the rack.
Summary
A research study analyzing AI coding assistants' tool recommendations by testing Claude Code on real repositories 2,430 times. The study examines what development tools Claude Code, Codex, and Cursor actually recommend when given open-ended questions without tool names in prompts. Key findings show Claude Code prefers custom/DIY solutions over pre-built tools, with an 85.3% extraction rate across 3 models, 4 project types, and 20 tool categories. The research includes a head-to-head benchmark comparison of OpenAI Codex against Claude Code with 1,470 responses across 12 categories.
Key quotes
· 4 pulledWe pointed Claude Code at real repos 2,430 times and watched what it chose. No tool names in any prompt. Open-ended questions only.
The big finding: Claude Code builds, not buys. Custom/DIY
3 models · 4 project types · 20 tool categories · 85.3% extraction rate
We benchmarked OpenAI Codex head-to-head against Claude Code — 1,470 responses across 12 new categories.
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